1. Field of the Invention
The present invention relates to a method of analyzing an image represented by a digital image representation.
2. Description of the Related Art
In a radiocontrast medical imaging setting, a patient is administered a contrast agent to increase the radiodensity of some lumen in the body. In a reconstruction of angiographic X-ray projections, the vessel tree will therefore have a density similar to that of bony tissue. As such, when displaying only high intensity voxels of the volume, the radiologist is presented with an image containing only the vessel tree and bone. As bone might visually obstruct certain parts of the vessel tree, a significant speed-up of the diagnosis can be achieved by removing the skeletal structures from the view. This task can be broken up in a segmentation, and a classification task. During segmentation, the image data is broken up into regions that contain image elements likely to be of the same type (i.e. bone or vessel). Based on some quantitative or qualitative features of the regions, a classification scheme or user then determines if a particular region should be considered osseous or vascular tissue.
For smooth integration into the radiologist's workflow, bone removal algorithms should be designed with performance in mind. Whether devising an automated or semi-automated technique, a radiologist should not have to wait prohibitively long for an initial or manually corrected result.
Current approaches to bone removal include both interactive and automated approaches. Some interactive techniques include the work of Alyassin et al. (Alyassin, A. M., Avinash, G. B.: Semiautomatic bone removal technique from CT angiography data. Med Imaging, Proc. SPIE 4322 (2001) 1273-1283) in which interactively controlled global thresholds are used to separate the two tissue types. Notable automated methods include the ones by Grau, V., Mewes, A. U. J., Alca{tilde over ( )}niz, M., Kikinis, R., Warfield, S. K.: Improved watershed transform for medical image segmentation using prior information. IEEE Trans Med Imaging 23(4) (2004) 447-458 and H. Hahn, M. Wenzel, O. Konrad-Verse, and H. O. Peitgen, “A minimally-interactive watershed algorithm designed for efficient CTA bone removal,” Computer Vision Approaches to Medical Image Analysis, pp. 178-189, 2006 who both used watershed transforms for segmentation. M. Fiebich, “Automatic bone segmentation technique for CT angiographic studies”, J. Comput As, vol. 23, no. 1, p. 155, 1999 uses an iterative region growing approach, but has trouble in dealing with vessel calcifications. To increase robustness in an automated scheme, several authors need to use prior information Grau, and also M. Straka, A. LaCruz, M. {hacek over (S)}ramek, E. Groller, L. I. Dimitrov, and D. Fleischmann, “Bone segmentation in CT-angiography data using a probabilistic atlas,” Proc. VMV 2003, vol. 121, 2003.
The watershed transform as used by Grau and Hahn is a powerful and commonly used segmentation tool. However, one pitfall of these (watershed methods), as stated in U.S. Pat. No. 6,985,612, is oversegmentation. The watershed techniques commonly generate too much basins, resulting in a high computational load during feature extraction and classification. There have been attempts to mitigate this, but the watershed transform remains a very costly operation on contemporary CTA (Computed Tomography Angiography) datasets. Therefore, the use of the watershed transform might be unwarranted when aiming for a fast algorithm. It has been noticed that in a vast majority of CTA studies, with the exception of head and neck studies, vascular and osseous tissue can reliably be separated by a simple thresholding operation. This can be explained by the fact that vessels visible in CTA studies are never anatomically fused to bone tissue; the high contrast lumen is always separated by soft tissue, in the very least by the vessel wall. It is due to partial volume effects and the limited resolution of CT modalities that they may appear to touch in CTA studies. Since the partial volume effect produces a voxel with intensity equal to a weighted average of the intensities of the different tissue types in that voxel, a threshold operation at the right intensity level is usually sufficient to separate bone and vascular components. Finding that right intensity level however, is not an easy task, which is why most existing approaches use interactively controlled threshold levels. The technique proposed here finds these levels automatically and then uses a trained classifier to determine the type of the segmented parts.